Project summary: The main objectives of this project are to perform longitudinal collection of clinical, behavioral, and neuroimaging data from a cohort of adolescents with depression and anxiety disorders, as well as healthy controls; and to develop a set of analytical tools that can be used to study the developmental trajectory of brain structure and function in this population. The project builds on the ongoing collaboration of our team in a Connectomes Related to Human Disease U01 project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, where we have been performing extensive clinical characterization and MRI scanning with Human Connectome Project (HCP) protocols on adolescents with depression and/or anxiety disorders and healthy controls. These baseline data (current total: N=170; final target: N=225) are set to become available publicly through the HCP database. Here we propose to collect longitudinal data on this unique, thoroughly characterized cohort. Following up on these subjects will allow us to investigate the complex relationship between longitudinal changes in neural circuitry and the onset, persistence, or recurrence of depression and anxiety disorders. We will tackle this by bringing together an investigative team with strong expertise in adolescent mood disorders and in neuroimaging data analysis. The MPIs have extensive experience in developing publicly available software tools for the analysis of brain connections from diffusion MRI (Yendiki) and functional MRI (Whitfield-Gabrieli). In this project, we propose to develop robust, automated tools for segmenting deep-brain structures and white-matter pathways that are of interest in psychiatric disorders. This development will build on our prior work in unbiased methods for longitudinal morphometric and tractography analyses. We will leverage the proposed longitudinal dataset and tools for accurate delineation of individual anatomy to perform a number of novel analyses that will go beyond conventional group-wise comparisons. Specifically, we will focus on analyses that allow us to predict clinical outcomes in individual subjects based on their neural circuitry. We will use machine-learning techniques to map the normative developmental trajectory of brain structure and function in healthy adolescents, including our controls and those from the development HCP. We will then investigate how and when the trajectories of individual adolescents with depression and/or anxiety disorders deviate from this normative trajectory. The longitudinal data set that we will collect and the software tools that we will develop will be shared with the research community. Our analysis methods will be applicable beyond this cohort, and could be used to study disease mechanisms and predict outcomes in a wide range of brain-related disorders across the human lifespan.